Upload 3 files

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load model
+model = tf.keras.models.load_model("dogcat_model.h5")
+
+def predict(image):
+    image = image.convert("RGB").resize((224,224))
+    image = np.array(image) / 255.0
+    image = np.expand_dims(image, axis=0)
+    pred = model.predict(image)[0][0]
+    return {"Cat": float(1-pred), "Dog": float(pred)}
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=2)
+)
+
+iface.launch()

dogcat_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bc8f0df68b4d988bcf4a8b0f999d557c7715fe825d5477a1ecac4c0536656e85
+size 231224216

requirements.txt

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+tensorflow
+gradio
+numpy
+pillow